Large Antenna Arrays Research Articles

Direction-of-Arrival Estimation for Large Antenna Arrays With Hybrid Analog and Digital Architectures

The large antenna arrays with hybrid analog and digital (HAD) architectures can provide a large aperture with low cost and hardware complexity, resulting in enhanced direction-of-arrival (DOA) estimation and reduced power consumption. This paper investigates the trade-off between DOA estimation and power consumption in large antenna arrays with HAD architectures. Particularly, the DOA estimation problem of fully-connected, sub-connected (SC), and switches-based (SE) hybrid architectures is formulated into a unified expression, with the compression matrix in a time-varying form. Based on this model, we derive a dynamic maximum likelihood (D-ML) estimator that is suitable for both HAD and conventional fully digital (FD) structures, and the closed-form expression of Cramér-Rao bound (CRB) to evaluate the performance limit of the D-ML estimator for different HAD structures. The theoretical CRB analysis in the single-source case reveals that, the SC structure has the ability to achieve approximately the same performance as the FD structures at DOAs around zero, but suffers from the inherent angle ambiguity because of the antenna grouping. In addition, we propose a dynamic SC (D-SC) structure that is proved to eliminate the angle ambiguity with time-varying phase shifters, and a switch optimization (SWO) algorithm to minimize the CRB of SE structures. Finally, we introduce a new metric, DOA efficiency, to measure the trade-off between the DOA estimation performance and power consumption of different structures. Simulation results verify our theoretical analysis and the superiority of the proposed D-SC structure and the SWO algorithm.

Clustered Double-Scattering Channel Modeling for XL-MIMO With Uniform Arrays

Extra-large multiple-input multiple-output (XL-MIMO) systems reveal themselves as a potential candidate for the sixth generation (6G) of wireless communication systems due to their features. As XL-MIMO has a large antenna array, and, typically, the urban environment is plenty of obstacles and reflectors, the spatial non-stationarities are introduced in the signal received at the base station (BS), which means that only a portion of the antenna array is visible to users; hence, accurately modeling XL-MIMO channels is paramount. Previous works on XL-MIMO channel modeling have adopted the non-stationarities only in a spatial sense, and do not consider spatial-time evolution scenarios. Moreover, simplified models with only one set of clusters between the BS and the user equipment (UE) are usually adopted; hence, there is a lack of understanding regarding such channels modeling in the literature. This work proposes and extensively analyzes a double-scattering XL-MIMO channel model, by admitting two types of scattering clusters, one placed at the BS side, and another one located near the UEs. In addition, two distinct antenna array configurations are included in the analysis, the uniform linear and planar arrays (ULA and UPA). We propose a new double-scattering channel model under UPA arrangements, suitable for modeling spatial non-stationarities scenarios in XL-MIMO dynamic environment subject to BS-cluster and UE-cluster correlation, and birth-death channel clusters and scatterers. Numerical results for signal-to-interference-plus-noise ratio (SINR), condition number (CN), and spectral efficiency (SE) performance metrics considering different XL-MIMO channels and system configurations are analyzed via Monte-Carlo simulations, under linear combiners MRC, ZF, and MMSE. Also, we characterize the impact of the number of visible clusters per user, the birth-death rate growth effect on the channel clusters and scatterers, and the favorable propagation effect according to the size of visibility region (VR) overlap. It can be observed that the birth and death processes have a significant impact on the system performance. Under the proposed clustered double-scattering channel modeling, the analyzed XL-MIMO linear receivers presented an SINR degradation around 3 to 4 dB for the MMSE when the number of UEs substantially increased, while the ZF and MRC receivers present a decrease of 1 to 2 dB, approximately, in SINR for the considered dynamic configuration compared with static scenarios.

Channel Charting Aided Pilot Reuse for Massive MIMO Systems With Spatially Correlated Channels

Massive multiple-input multiple-output (mMIMO) technology is a way to increase the spectral efficiency of machine-type communications (MTC). To exploit the benefits from large antenna arrays, accurate channel estimation through pilot signals is needed. Massive MTC systems cannot avoid pilot reuse due to the enormous numbers of connected devices. We propose a pilot reuse algorithm based on channel charting (CC) to mitigate pilot contamination in a multi-sector single-cell massive MTC system having spatially correlated channels. We show that after creating an interference map via CC, a simple strategy to allocate the pilot sequences can be implemented. The simulation results show that the CC-based pilot reuse strategy improves channel estimation accuracy, which subsequently improves the symbol detection performance and increases the spectral efficiency compared to other existing schemes. Moreover, the performance of the CC pilot assignment method approaches that of exhaustive search pilot assignment for small network setups.

Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Synchronization is a fundamental procedure in cellular systems whereby user equipment (UE) acquires the time and frequency information required to decode the data transmitted by a base station (BS). Due to the small antenna aperture at millimeter wave (mmWave), large antenna arrays are used to compensate for the low signal-to-noise ratio (SNR) at the receiver. For this reason, synchronization is often performed jointly with beam training as in 5G New Radio (NR) to boost the receive SNR. However, if the mmWave multiple-input multiple-output (MIMO) channel is to be estimated, then synchronization must be performed in the low SNR regime owing to the lack of directional beamforming during compressive channel estimation. Current mmWave channel estimation algorithms implicitly take synchronization for granted, which is a not practical assumption in the low SNR regime. To solve this problem, this work proposes the first synchronization framework for hybrid mmWave MIMO that is robust to carrier frequency offset (CFO) and phase noise (PHN) synchronization errors. We propose two novel multi-stage algorithms to jointly estimate the various unknown parameters, based on the expectation-maximization (EM) algorithm. By using the QuaDRiGa 5G NR channel simulator, we show that compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.

An Efficient Machine Learning Based Precoding Algorithm for Millimeter-Wave Massive MIMO

Millimeter wave communication works in the 30–300 GHz frequency range, and can obtain a very high bandwidth, which greatly improves the transmission rate of the communication system and becomes one of the key technologies of fifth-generation (5G). The smaller wavelength of the millimeter wave makes it possible to assemble a large number of antennas in a small aperture. The resulting array gain can compensate for the path loss of the millimeter wave. Utilizing this feature, the millimeter wave massive multiple-input multiple-output (MIMO) system uses a large antenna array at the base station. It enables the transmission of multiple data streams, making the system have a higher data transmission rate. In the millimeter wave massive MIMO system, the precoding technology uses the state information of the channel to adjust the transmission strategy at the transmitting end, and the receiving end performs equalization, so that users can better obtain the antenna multiplexing gain and improve the system capacity. This paper proposes an efficient algorithm based on machine learning (ML) for effective system performance in mmwave massive MIMO systems. The main idea is to optimize the adaptive connection structure to maximize the received signal power of each user and correlate the RF chain and base station antenna. Simulation results show that, the proposed algorithm effectively improved the system performance in terms of spectral efficiency and complexity as compared with existing algorithms.

A Folding Approach for Multiple Antenna Arrays Using Low-Resolution ADCs

Both massive multiple-input multiple-output (MIMO) and millimeter wave (mmWave) systems are receiving much attention for 5G and beyond-5G wireless access. When using a large antenna array, there are numerous challenges in designing low-power and cost-effective hardware. Most notably, the traditional approach of using high-resolution analog-to-digital converters (ADCs) at each element leads to both costly and high-power consumption devices. This has motivated research on multiple antenna arrays that use a low-resolution ADC at each element. We present a new framework for designing a multiple antenna array using a low-resolution ADC at each element, with the low-resolution ADC composed of simple shift and modulo analog processing and a sign quantizer. This modulo technique, called folding, has previously been utilized in the design of high-speed high-resolution ADCs. Using theory from folded ADCs, we show how to jointly design the modulo processing used across the elements. We show that our framework can provide substantial rate improvements including providing a linear growth in rate with the number of antennas at high SNR, as opposed to the logarithmic growth with the number of antennas provided by a sign-based low-resolution ADC array. We also explore how antenna ordering strategies can be used. We utilize Monte Carlo simulations to back our analysis.

A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking

Unmanned aerial vehicles (UAVs) have found widespread commercial, civilian, and military applications. Wireless communication has always been one of the core technologies for UAV. However, the communication capacity is becoming a bottleneck for UAV to support more challenging application scenarios. The heavily-occupied sub-6 GHz frequency band is not sufficient to meet the ultra high-data-traffic requirements. The utilization of the millimeter-wave (mmWave) frequency bands is a promising direction for UAV communications, where large antenna arrays can be packed in a small area on the UAV to perform three-dimensional (3D) beamforming. On the other hand, UAVs serving as aerial access points or relays can significantly enhance the coverage and quality of service of the terrestrial mmWave cellular networks. In this paper, we provide a comprehensive survey on mmWave beamforming enabled UAV communications and networking. The technical potential of and challenges for mmWave-UAV communications are presented first. Then, we provide an overview on relevant mmWave antenna structures and channel modeling. Subsequently, the technologies and solutions for UAV-connected mmWave cellular networks and mmWave-UAV ad hoc networks are reviewed, respectively. Finally, we present open issues and promising directions for future research in mmWave beamforming enabled UAV communications and networking.

Improved Iterative Inverse Matrix Approximation Algorithm for Zero Forcing Precoding in Large Antenna Arrays

Precoding algorithms are used in massive multiple-input multiple-output (mMIMO) communication systems to ensure effective signal transmission. The zero-forcing (ZF) is one of the most common linear precoding algorithms used in MIMO and mMIMO systems. ZF precoding is complex to implement because it requires direct matrix inversion of the gram matrix. Iterative algorithms have been proposed for approximating matrix inversions. However, iterative algorithms require initial conditions and pre-computations to converge to the optimal transmitted signal vector. This paper proposes a new improved iterative algorithm that guarantees convergence under any circumstances without dependency on any optimized initial parameter or condition. The proposed algorithm is based on a three-step iterative and iterative generalized inverse matrix approximation algorithm. The proposed algorithm was verified using a new correlated channel model that included mutual coupling effects and gain and phase variances caused by radio frequency elements at a base station (BS). The computational complexity of the proposed algorithm was then computed. This study analyzes and compares the bit error rate (BER) performance of the proposed algorithm with that of prominent existing algorithms. Moreover, the sum-rate performance of the proposed algorithm was analyzed. Simulations were performed under both correlated and uncorrelated channel conditions, for comparison and analysis. The simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of the convergence and convergence rates.

Dictionary-Learning (DL)-Based Sparse CSI Estimation in Multiuser Terahertz (THz) Hybrid MIMO Systems Under Hardware Impairments and Beam-Squint Effect

This work conceives dictionary-learning (DL)-based sparse channel estimation schemes for multi-user Terahertz (THz) hybrid MIMO systems incorporating also non-idealities such as hardware impairments and beam-squint effect. Due to the presence of large antenna arrays coupled with frequency selectivity, beam squint effect is significant in THz systems. Moreover, the manufacturing and calibration errors that inevitably arise during the production of antenna arrays result in hardware impairments such as irregular antenna spacing, mutual coupling and antenna gain/phase errors in practical THz systems. To overcome these problems, this work proposes a DL algorithm to determine the best sparsifying dictionary from the acquired observations for a single-carrier frequency domain equalization (SC-FDE)-based wideband THz system in the presence of hardware impairments as well as the beam squint effect. The dictionary thus obtained is subsequently employed to exploit the sparsity of the MIMO THz channel toward CSI estimation. Furthermore, the Cramér-Rao lower bound (CRLB) is also derived for the joint DL and CSI estimation algorithm, which acts as a benchmark for the mean-squared error (MSE) performance of the channel estimate obtained. The scheme is also extended to SC-FDE-based wideband THz MIMO systems with multiple antenna users. Simulation results are presented to corroborate our analytical findings and also demonstrate the improved performance with respect to the agnostic scheme that ignores the non-idealities.

Spatial Characterization of Electromagnetic Random Channels

The majority of stochastic channel models rely on the electromagnetic far-field assumption, which allows to decompose the channel in terms of plane waves. The far-field assumption breaks down in future applications that push towards the electromagnetic near-field region, such as those where the use of electromagnetically large antenna arrays is envisioned. Motivated by this consideration, we show how physical principles can be used to derive a plane-wave scalar channel model that is also valid in the reactive near-field region. Precisely, we show that narrowband wave propagation through a three-dimensional scattered medium can be generally modeled as a linear and space-variant system. We first review the physics principles that lead to a closed-form deterministic plane-wave representation of the channel impulse response. This serves as a basis for deriving a stochastic representation of the channel in terms of statistically independent Gaussian random coefficients for spatially stationary random propagation environments. The very desirable property of spatial stationarity can always be retained in the radiative near-field region by excluding reactive propagation mechanisms confined in close proximity to the source. Remarkably, the provided stochastic representation is directly connected to the Fourier spectral representation of a general stationary spatial random field.

Novel Multilayer Extreme Learning Machine as a Massive MIMO Receiver for Millimeter Wave Communications

Wireless communication systems working in millimeter-wave (mmWave) frequency bands offer higher bandwidths than traditional radio frequency schemes. This technology allows multibeam steering and data multiplexing with the help of massive multiple-input multiple-output (MIMO) systems. However, supporting large bandwidths at mmWave frequencies is challenging due to the use of large antenna arrays with beamforming, sampling signals with large bandwidths, and baseband signal processing operations at gigabit data rates. Due to the wider bandwidth and higher signal processing requirements of mmWave systems, low-complexity receiver algorithms become important. Previously reported investigations assumed the use of hybrid beamforming structures that reduce power consumption and signal processing tasks. Therefore, the use of artificial neural networks (ANNs) becomes relevant for the processing of mmWave signals as reported in earlier works. In this article, to carry out MIMO combining processing for mmWave communications, we propose a fully complex multilayer extreme learning machine (M-ELM) neural network. We investigate the tuning of the number of neurons in each hidden layer for the proposed method to maximize the system performance and decrease the complexity of the receiver. We compare the results of the introduced M-ELM algorithm with a fully complex extreme learning machine (ELM), fully real ELM, and M-ELM defined in the real plane in terms of spectral efficiency, bit error rate, computational complexity, and processing time. Furthermore, we compare the novel M-ELM strategy with traditional linear MIMO receivers, such as Maximum Ratio and Minimum Mean Square Error, as well as to a multilayer perceptron (MLP) neural network trained offline. The numerical results show that with a good balance between the overall performance and computational cost of the ANN, the fully complex M-ELM MIMO receiver outperforms the other evaluated schemes.

Low Computational Complexity for Optimizing Energy Efficiency in mm-wave Hybrid Precoding System for 5G

Millimeter-wave (mm-wave) communication is the spectral frontier to meet the anticipated significant volume of high data traffic processing in next-generation systems. The primary challenges in mm-wave can be overcome by reducing complexity and power consumption by large antenna arrays for massive multiple-input multiple-output (mMIMO) systems. However, the circuit power consumption is expected to increase rapidly. The precoding in mm-wave mMIMO systems cannot be successfully achieved at baseband using digital precoders, owing to the high cost and power consumption of signal mixers and analog-to-digital converters. Nevertheless, hybrid analog–digital precoders are considered a cost-effective solution. In this work, we introduce a novel method for optimizing energy efficiency (EE) in the upper-bound multiuser (MU) - mMIMO system and the cost efficiency of quantized hybrid precoding (HP) design. We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). In the alternating minimization algorithms, low complexity is proposed by enforcing an orthogonal constraint on the digital precoders to realize the joint optimization of computational complexity and communication power. Therefore, the alternating minimization algorithm enhances HP by improving the performance of the FCS through advanced phase extraction, which involves high complexity. Meanwhile, the alternating minimization algorithm develops a PCS to achieve low complexity using HP. The simulation results demonstrate that the proposed algorithm for MU - mMIMO systems improves EE. The power-saving ratio is also enhanced for PCS and FCS by 48.3% and 17.12%, respectively.

Optimization of Coupled Periodic Antenna Using Genetic Algorithm with Floquet Modal Analysis and MoM-GEC

In this paper Genetic Algorithm has been integrated with Fouquet modal analysis to optimize radiation pattern of coupled periodic antenna. Floquet analysis is used with MoM-GEC (Moment-Generalized Equivalent Circuit) method to study a finite periodic array with uniform amplitude and linear phase distribution. This method is very advantageous for studying large antenna array since it considerably reduces the computation time and the number of operations. In this way, Genetic algorithm is introduced and combined with Floquet analysis to optimize the radiation pattern distribution of this coupled periodic antenna. The goal of the optimization is to provide a better radiation characteristic for the coupled periodic antenna with maximum side lobe level reduction.

Cramér-Rao Bounds for Holographic Positioning

Multiple antennas arrays combined with high carrier frequencies play a key role in wireless networks for communications but also for localization and sensing applications. To understand the fundamental limits of electromagnetically large antenna arrays for localization, this paper combines wave propagation theory with estimation theory, and computes the Cramér-Rao Bound (CRB) for the estimation of the source position on the basis of the three Cartesian components of the electric field, observed over a rectangular surface area. The problem is referred to as holographic positioning and it intrinsically depends on the radiation angular pattern of the transmitting source, which is typically ignored in standard signal processing models. We assume that the source is a Hertzian dipole, and address the holographic positioning problem in both cases, that is, with and without a priori knowledge of its orientation. To simplify the analysis and gain further insights, we also consider the case in which the dipole is located on the line perpendicular to the surface center. Numerical and asymptotic results are given to quantify the CRBs, and to quantify the effect of various system parameters on the ultimate estimation accuracy. It turns out that square surfaces with side comparable to the distance are needed to guarantee a centimeter-level accuracy in the mmWave bands. Moreover, we show that the CRBs with and without a priori knowledge of the source dipole orientation are numerically the same. The provided CRBs are also used to benchmark different maximum-likelihood estimators (MLEs) derived on the basis of a discrete representation of different models of the electric field. The analysis shows that, if the standard models are used (neglecting the radiation angular pattern), the MLE accuracy is far from the CRB. On the other hand, it approaches the CRB when the more detailed electromagnetic model is considered.

Development of a 70–110 GHz silicon corrugated horn for cosmic microwave background experiment

Next generation cosmic microwave background (CMB) polarization telescope will use large arrays of receiver with nearly 100 000 transition edge sensor (TES) detectors and receiving antennas. Such large TES and antenna array is a big challenge for the fabrication of TES and antennas. A 35-layers silicon-platelet corrugated horn for 70–110 GHz is designed and fabricated based on the micro electro mechanical system (MEMS) technology. Compared with the traditional corrugated horn, the last layer of the 35-layers silicon-platelet is designed with no corrugation to increase the beam symmetry. A parametric HFSS model of the horn was established to evaluate the performance of the horn and effect of fabrication process. The measured S11 and cross-polarization of the fabricated prototype horn are both below −20 dB while the sidelobe at 70 and 90 GHz is below −20 dB. The beam symmetry of the horn is good in 70–110 GHz band. The measured gain is greater than 15 dBi. These results show that the performance of the designed horn meets the requirement of CMB experiment.

Efficient Cell-Specific Beamforming for Large Antenna Arrays

We propose an efficient method for designing broad beams with spatially flat array factor and efficient power utilization for cell-specific coverage in communication systems equipped with large antenna arrays. To ensure full power efficiency, the method is restricted to phase-only weight manipulations. Our framework is based on the discovered connection between dual-polarized beamforming and polyphase Golay sequences. Exploiting this connection, we propose several methods for array expansion from smaller to larger sizes, while preserving the radiation pattern. In addition, to fill the gaps in the feasible array sizes, we introduce the concept of ϵ-complementarity that relaxes the requirement on zero side lobes of the sum aperiodic autocorrelation function of a sequence pair. Furthermore, we develop a modified Great Deluge algorithm (MGDA) that finds ϵ-complementary pairs of arbitrary length, and hence enables broad beamforming for arbitrarily-sized uniform linear arrays. We also discuss the extension of this approach to two-dimensional uniform rectangular arrays. Our numerical results demonstrate the superiority of the proposed approach with respect to existing beam-broadening methods.

극소 다이폴 모델링 및 최소자승법을이용한 능동 배열안테나의 근접전계 빔패턴 재구성 및 합성방법

In future-oriented communication, such as wireless power transmission or 6G, large array antennas are used to maximize energy and signal transmission efficiency in the near field. Therefore, the analysis of the near-field beam pattern of an array antenna is a very important research field. In this paper, we propose a method to accurately and efficiently reconstruct the near-field beam pattern, which is difficult to analyze, using the infinitesimal dipole modeling (IDM) of an array antenna. First, IDMs are extracted from the active element pattern (AEP) of the far-field acquired through measurement or simulation, and the mutual coupling effect of the array antenna is reflected in the IDM. Near-field active element patterns (NAEP) can be obtained by performing electromagnetic wave re-radiation for an observation area preset for each of the current distributions of IDMs. Subsequently, a method to easily and accurately analyze the desired near-field electric beam pattern through the combination of the corresponding NAEP and beam coefficient is proposed. Moreover, a method to efficiently extract the beam coefficients using the least squares method and a mask is proposed. Using the obtained beam coefficients, a near-field beam pattern can be synthesized into the desired shape. The proposed method is described in detail using a simulation of a 12-dipole linear array antenna as an example.ample.

Multi-Domain Channel Extrapolation for FDD Massive MIMO Systems

Future mobile systems have shown a growing trend towards wider frequency bands, larger antenna arrays, and more user equipment, simultaneously expanding the channel in the frequency, space, and user domains. However, the huge size of the multi-domain channel brings great challenges to the acquisition of channel state information (CSI), especially in frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems. In this paper, we propose a multi-domain channel extrapolation scheme that can reconstruct the huge multi-domain channel with low pilot overhead. Specifically, information on the environment shared by multiple domains is utilized for the design of a low-complexity channel extrapolation algorithm. Moreover, we investigate the patterns of sparse pilots and antenna selection by establishing a theoretical framework for the performance analysis of the patterns. We further propose a sparse random pattern design, which can legitimately obtain a set of patterns that are suitable for channel extrapolations. Numerical results demonstrate that we can accurately extrapolate the multi-domain channel using our proposed channel extrapolation scheme and our designed sparse random patterns.

Accuracy Versus Complexity for mmWave Ray-Tracing: A Full Stack Perspective

The millimeter wave (mmWave) band will provide multi-gigabits-per-second connectivity in the radio access of future wireless systems. The high propagation loss in this portion of the spectrum calls for the deployment of large antenna arrays to compensate for the loss through high directional gain, thus introducing the need for a spatial dimension in the channel model to accurately represent the performance of a mmWave network. In this perspective, ray tracing can characterize the channel in terms of Multi Path Components (MPCs) to provide a highly accurate model, at the price of extreme computational complexity (e.g., for processing detailed environment information about the propagation), which may limit the scalability of the simulations. In this paper, we present possible simplifications to improve the trade-off between accuracy and complexity in ray-tracing simulations at mmWaves by reducing the total number of MPCs. The effect of such simplifications is evaluated from a full-stack perspective through end-to-end simulations, testing different configuration parameters, propagation scenarios, and higher-layer protocol implementations. We then provide guidelines on the optimal degree of simplification, for which it is possible to reduce the complexity of simulations with a minimal reduction in accuracy for different deployment scenarios.

MIMO Evolution toward 6G: Modular Massive MIMO in Low-Frequency Bands

As the pace of global 5G network deployments accelerates, now is the moment for the cellular industry to realize 6G cellular communication. In this article, modular massive multiple-in-put multiple-output (mmMIMO) is presented as one candidate technology for 6G to improve the spectral efficiency in low-frequency bands. The 5G New Radio pushed the boundary of the cellular system's operating frequency to high-frequen-cy bands, and this trend will continue in the 6G era. However, the technical advances in 5G for low-frequency bands fall short, although low-frequency bands are crucial in serving a large number of users in a wide coverage area. Although it would be ideal if massive MIMO could be utilized in low-frequency bands, it is less practical due to a large antenna form factor size. mmMIMO is a technology to distribute a large active antenna array with smaller standardized antenna modules, just like Lego-type building blocks. Through this, the benefits of massive MIMO can be achieved in low-frequency bands (e.g., sub-1 GHz), unconstrained by spatial limitations. In this article, the concept of mmMIMO, its applicability, and needed research efforts to standardize the technology for 6G are discussed. In addition, through the demonstration of a proof-of-concept system, it is shown that the technology can be within reach at the time of 6G commercialization around 2030. Lastly, the performance gain of mmMIMO is evidenced by system-level simulation.